Method for predicting morphological changes of liver tumor after ablation based on deep learning

ABSTRACT

A method for predicting the morphological changes of liver tumor after ablation based on deep learning includes: obtaining a medical image of liver tumor before ablation and a medical image of liver tumor after ablation; preprocessing the medical image of liver tumor before ablation and the medical image of liver tumor after ablation; obtaining a preoperative liver region map, postoperative liver region map, and postoperative liver tumor residual image map; obtaining a transformation matrix by a Coherent Point Drift (CPD) algorithm and obtaining a registration result map according to the transformation matrix; training the network by a random gradient descent method to obtain a liver tumor prediction model; using the liver tumor prediction model to predict the morphological changes of liver tumor after ablation. The method provides the basis for quantitatively evaluating whether the ablation area completely covers the tumor and facilitates the postoperative treatment plan for the patient.

CROSS REFERENCE TO THE RELATED APPLICATIONS

This application is the national phase entry of InternationalApplication No. PCT/CN2020/125768, filed on Nov. 2, 2020, which is basedupon and claims priority to Chinese Patent Application No.201911067810.8, filed on Nov. 4, 2019, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present invention relates to the field of minimally invasiveablation, particularly to a method for predicting morphological changesof liver tumor after ablation based on deep learning.

BACKGROUND

In recent years, image-guided percutaneous thermal ablation has becomeone of the most promising minimally invasive treatment methods for solidtumors such as liver, kidney and breast. Among them, microwave ablationis guided by ultrasound, computed tomography (CT) and other images, theablation needle is inserted into the tumor, and the local polarmolecules are vibrated and rubbed to produce high temperature byreleasing electromagnetic wave, so as to achieve the purpose of tumorinactivation. Compared with the traditional surgery, it has theadvantages of small trauma, good curative effect, fast recovery,repeatability, low cost, and can improve the immune function of thebody. It can achieve a good curative effect of completely inactivatingthe tumor without surgery. However, different from the clear view oftumor and treatment area in open surgery, minimally invasive ablation isconducted under the guidance of intraoperative two-dimensional images.Whether the treatment area completely covers the tumor and reachesenough safety boundary needs to be evaluated by comparing thepreoperative and postoperative images. However, due to the changes ofpatients' postures before and after surgery and the soft tissuedeformation caused by the treatment process, including the tissuecontraction in the ablation inactivated area and the expansion of liverregeneration volume, it is impossible to accurately evaluate thecurative effect only by preoperative and postoperative two-dimensionalimage comparison or three-dimensional rigid registration. Therefore, itis a difficult problem to realize the accurate curative effectevaluation of tumor ablation in three-dimensional space throughpreoperative and postoperative images.

In recent years, point cloud registration is a common registrationmethod for preoperative and postoperative three-dimensional imageevaluation. At present, a large number of methods have been studied for3D image registration, among which the iterative closest point (ICP) isthe most classic and commonly used algorithm. Most of the follow-upalgorithms are based on it. However, no matter which method, in theregistration process, only the image's own characteristics areconsidered, and the changes of local tissue shrinkage caused by hightemperature in the clinical treatment process are not taken intoaccount, which leads to the inaccuracy of the evaluation results.

Deep learning is derived from artificial neural network. It can graspthe inherent law through the learning of sample data, and then realizethe speculation and judgment of similar data. It is the only way torealize artificial intelligence. With the development of medicalinformatization and digital diagnosis, and the increasing amount ofmedical data, deep learning may provide strong support for the medicalfield.

SUMMARY

In order to solve the above-mentioned problems of the prior art, theinvention aims to provide a method for predicting the morphologicalchange of liver tumor after ablation based on deep learning, which canassist doctors to evaluate a curative effect after operation and lay afoundation for the development of a follow-up treatment plan.

The technical solution of the present invention is that a method forpredicting the morphological changes of liver tumor after ablation basedon deep learning, includes the following steps:

obtaining a medical image of liver tumor before ablation and a medicalimage of liver tumor after ablation;

preprocessing the medical image of liver tumor before ablation and themedical image of liver tumor after ablation;

obtaining a preoperative liver region map and a preoperative liver tumorregion map from a medical image map before ablation, and obtaining apostoperative liver region map, a postoperative ablation region map anda postoperative liver tumor residual image map from the medical imagemap after ablation;

registering the preoperative liver region map and the postoperativeliver region map by a Coherent Point Drift (CPD) algorithm, andobtaining a transformation matrix; obtaining a registration result mapof the medical image map after ablation corresponding to thepreoperative liver region map and the preoperative liver tumor regionmap according to the transformation matrix;

using the medical image map before ablation, the preoperative liverregion map, the preoperative liver tumor region map and the registrationresult map as an input of U-net network, and using the postoperativeliver tumor residual image map as a real training label; training thenetwork by a random gradient descent method to obtain a liver tumorprediction model; and using the liver tumor prediction model to predictthe morphological changes of liver tumor after ablation.

As a preferred method, the medical image comprises images obtained by CTand magnetic resonance imaging (MRI).

As an optimal method, the pre-processing of the medical image of livertumor before ablation and the medical image of liver tumor afterablation is as follows: dividing the data set into groups according toinfluencing factors of liver, then reading the medical image of livertumor before ablation and the medical image of liver tumor afterablation, and processing the medical images by Gaussian de-noising, grayhistogram equalization, image contrast enhancement, rotation, flippingand data standardization.

As a preferred method, the liver influencing factors comprise a liverstate, a tumor type and a pathological type.

As the preferred method, the obtaining of a preoperative liver regionmap and a preoperative liver tumor region map from a medical image mapbefore ablation, and the obtaining of a postoperative liver region map,a postoperative ablation region map and a postoperative liver tumorresidual image map from the medical image map after ablation are:marking the preoperative liver region map, preoperative liver tumorregion map and postoperative liver region map by a maximum flow/minimumcut algorithm; introducing a potential energy field function based on aglobal and local region representation as a constraint in a segmentationprocess, and establishing an adaptive hybrid variational model; using amaximum flow/minimum cut algorithm to solve an energy equationminimization; determining a target region selectively according to grayinformation, boundary gradient, texture information and local contextinformation in different image regions.

As the preferred method, the registering of the preoperative liverregion map and the postoperative liver region map by a CPD algorithm,and obtaining of a transformation matrix; obtaining of a registrationresult map of the medical image map after ablation corresponding to thepreoperative liver region map and the preoperative liver tumor regionmap according to the transformation matrix are: obtaining liver datapoints set of the preoperative liver region map and the postoperativeliver region map, making the preoperative liver data points set of thepreoperative liver region map X_(i)=(x₁, . . . , x_(N))^(T) as a targetpoint set, and making the postoperative liver data points set of thepostoperative liver region map Y₁=(y₁, . . . , y_(M))^(T) as a modelpoint set; the target point set is the data set of Gaussian mixturemodel, and the model point set is the kernel point set of Gaussianmixture model; N and M represent the number of the target point set andtemplate point set respectively; a probability density function ofGaussian mixture model is

${{p\left( {x{❘m}} \right)} = {\frac{1}{\left( {2{\prod\sigma^{2}}} \right)^{D\text{/2}}}\exp^{\frac{{{x - y_{m}}}_{2}^{2}}{2\sigma^{2}}}}},$${{p(x)} = {{\omega\frac{1}{N}} + {\left( {1 - \omega} \right){\sum\limits_{m = 1}^{M}{\frac{1}{M}{p\left( {x{❘m}} \right)}}}}}};$wherein p(x|m) is a probability density basis function of Gaussianmixture model, σ represents a standard deviation of the Gaussianprobability density function, ω represents a weight value of overflowpoints, and a value range is 0-1, x is a translation variable;calculating a minimum negative logarithm likelihood function:E(θ,σ²)=−Σ_(n=1) ^(N) log Σ_(m=1) ^(M+1) P(m)p(x|m),

wherein θ represents transformation parameters, and a represents thestandard deviation of the Gaussian probability density function;

obtaining the derivative according to a gradient descent method:

${{Q\left( {\theta,\sigma^{2}} \right)} = {{\frac{1}{2\sigma^{2}}{\sum\limits_{n = 1}^{N}{\sum\limits_{m = 1}^{M}{{P^{old}\left( {m{❘x_{n}}} \right)}{{x_{n} - {\tau\left( {y_{m},\theta} \right)}}}^{2}}}}} + {\frac{N_{p}D}{2}\log\sigma^{2}}}},$

wherein

${{P(m)} = \frac{1}{M}},$${N_{P} = {{\sum_{n = 1}^{N}{\sum_{m = 1}^{M}{P^{old}\left( {m{❘x_{n}}} \right)}}} \leq N}},$${{P^{old}\left( {m{❘x_{n}}} \right)} = \frac{\exp^{{- \frac{1}{2}}{\frac{x_{n} - {\tau({y_{m},\theta^{old}})}}{\sigma^{old}}}^{2}}}{{\sum_{k = 1}^{M}\exp^{{- \frac{1}{2}}{\frac{x_{n} - {\tau({y_{k},\theta^{old}})}}{\sigma^{old}}}^{2}}} + c}},$

τ represents one of rigid, affine and non-rigid transformations, and

${c = {\left( {2{\prod\sigma^{2}}} \right)^{D/2}\frac{\omega}{1 - \omega}\frac{M}{N}}},$

solving optimal parameters of the model of the minimum negativelogarithm likelihood function by iteration with anExpectation-Maximization algorithm; finally, calculating a location ofthe preoperative liver region map corresponding to the postoperativemedical image map according to the selected point cloud data andtransformation parameters.

As the preferred method, the using of the medical image map beforeablation, the preoperative liver region map, the preoperative livertumor region map and the registration result map as an input of U-netnetwork, and using of the postoperative liver tumor residual image mapas a real training label; training of the network by a random gradientdescent method to obtain a liver tumor prediction model are as follows:composing four channels of image data with the medical image beforeablation, the preoperative liver region map, the preoperative livertumor region map and the registration result map, and then inputtinginto U-net for coding to obtain a pixel classification probability map;determining the pixels with probability≥0.5 in the pixel classificationprobability map as liver tumors, and the pixels with probability≤0.5 asbackground, and finally obtaining a predicted liver tumor region.

As the preferred method, obtaining a pixel classification probabilitymap is as follows: composing four channels of image data with themedical image before ablation, the preoperative liver region map, thepreoperative liver tumor region map and the registration result map, andthen inputting into U-net for coding; convoluting the input data twice,and the number of output channels of each convolution is 64, andobtaining a feature map f1, which is performed for maximum pooling;after convoluting twice, the number of output channels of eachconvolution is 128, and obtaining a feature map f2, which is performedfor maximum pooling, after convoluting twice, the number of outputchannels of each convolution is 256, and obtaining a feature map f3,which is performed for maximum pooling; after convoluting twice, thenumber of output channels of each convolution is 512, and obtaining afeature map f4, which is performed for maximum pooling; afterconvoluting twice, the number of output channels of each convolution is1024, and obtaining a feature map f5, which is the encoding process;then, decoding f5, that is, up sampling f5 and performing concat withf4, after convoluting twice, the number of output channels of eachconvolution is 512, and obtaining a feature map f4_1; up sampling f4_1and performing concat with 3, after convoluting twice, the number ofoutput channels of each convolution is 256, and obtaining a feature map3_; up sampling 3_1 and performing concat with f2, after convolutingtwice, the number of output channels of each convolution is 128, andobtaining a feature map f2_1; up sampling f2_1 and performing concatwith f1, after convoluting twice, the number of output channels of eachconvolution is 64, and after convoluting once, the number of outputchannels is 2 to obtain the pixel classification probability map; theconcat is to connect the two feature maps in the channel dimension.

As a preferred method, the convolution is as follows: X_(j)^(i)=ƒ(Σ_(i∈M) _(j) x_(i) ^(l−1)*k_(ij) ^(l)+b_(j) ^(l)), wherein xrepresents an input characteristic channel of an accretion layer, Xrepresents an output characteristic channel of the accretion layer, krepresents a parameter of the convolution layer, b represents an offsetterm of the convolution layer; the symbol * represents a convolutionoperator; l represents a number of layers, i represents the i^(th)neuron node of l−1 layer, j represents the j^(th) neuron node of layer,represents a set of selected input characteristic graphs, x_(i) ^(l−1)refers to an output of l−1 layer as an input of l layer, ƒ represents anactivation function, the maximum pooling is to select the maximum valuein a region to represent characteristics of the region.

As a preferred method, constructing Dice loss function in a process ofnetwork training to alleviate the imbalance of background and foregroundpixels; the Dice loss function is as follows:

${d = {1 - {2\frac{pt}{p + t}}}},$wherein p represents a predicted liver tumor region and t represents areal liver tumor region.

The beneficial effects of the invention are as follows:

the invention provides a method for predicting the morphological changesof liver tumor after ablation based on deep learning, which solves theproblem of curative effect evaluation after ablation. It predicts themorphological changes of liver tumor after ablation with CPD point setregistration and U-net network. Through the registration of livercontour before and after ablation, the transformation relationship isobtained, and then the location of preoperative liver tumor inpostoperative CT/MRI image is obtained. Finally, the morphologicalchanges of liver tumor after ablation are predicted by U-net network.The invention can predict the morphological changes of liver tumor afterablation, provide the basis for quantitatively evaluating whether theablation area completely covers the tumor, facilitate the doctor toaccurately evaluate the postoperative curative effect, and lay thefoundation for the follow-up treatment plan of the patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of the present invention.

FIG. 2 is an interactive segmentation result diagram of the presentinvention.

FIG. 3 is the CPD registration result diagram of the present invention.

FIG. 4 is a U-net network structure diagram of the present invention.

FIG. 5 is the result diagram of the tumor deformation correction in theinvention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

As shown in FIG. 1 , this embodiment provides a method for predictingthe morphological changes of liver tumor after ablation based on deeplearning, including the following steps:

Step 1, CT/MRI scanning sequence images of liver tumor before and afterablation are obtained.

Step 2, the medical images before and after ablation are preprocessed asfollows: the data set is divided into groups according to theinfluencing factors of liver, then CT/MRI scanning sequence imagesbefore and after ablation is read, and CT/MRI scanning sequence imagesis processed by Gaussian de-noising, gray histogram equalization, imagecontrast enhancement, rotation, flipping and data standardization, toincrease the diversity of samples and accelerate the convergence speedof the network, wherein the liver influencing factors include liverstate, tumor type, pathological type and other factors.

Step 3, the preoperative liver region map and preoperative liver tumorregion map are obtained from the medical image map before ablation, thepostoperative liver region map, postoperative ablation region map andpostoperative liver tumor residual image map are obtained from themedical image map after ablation. As shown in FIG. 2 , specifically: thepreoperative liver region map, preoperative liver tumor region map andpostoperative liver region map are marked by the maximum flow/minimumcut (Max-Flow/Min-Cut) algorithm. In the segmentation process, thepotential energy field function based on global and local regionrepresentation is introduced as a constraint, and the adaptive hybridvariational model is established. The maximum flow/minimum cut algorithmis used to solve the energy equation minimization. The target region isdetermined selectively according to the gray information, boundarygradient, texture information and local context information in differentimage regions. The simple interaction on single-layer data can quicklycomplete 3D segmentation, and the doctor can modify the segmentationresult manually to complete image annotation efficiently and accurately.

Step 4, the CPD algorithm is used to register the preoperative liverregion map and the postoperative liver region map, and thetransformation matrix is obtained. According to the transformationmatrix, the registration result map of the preoperative liver region mapand the preoperative liver tumor region map corresponding to the medicalimage map after ablation are obtained, specifically:

-   -   Obtaining the liver data points set of the preoperative liver        region map and the postoperative liver region map, making the        preoperative liver data points set of the preoperative liver        region map X_(i)=(x₁, . . . , x_(N))^(T) as the target point        set, and making the postoperative liver data points set of the        postoperative liver region map Y_(i)=(y₁, . . . , y_(M))^(T) as        the target point set. The target point set is the data set of        Gaussian mixture model, and the template point set is the kernel        point set of Gaussian mixture model. N and M represent the        number of target point set and template point set respectively.        The probability density function of Gaussian mixture model is

${{p\left( {x{❘m}} \right)} = {\frac{1}{\left( {2{\prod\sigma^{2}}} \right)^{D\text{/2}}}\exp^{\frac{{{x - y_{m}}}_{2}^{2}}{2\sigma^{2}}}}},$${{p(x)} = {{\omega\frac{1}{N}} + {\left( {1 - \omega} \right){\sum\limits_{m = 1}^{M}{\frac{1}{M}{p\left( {x{❘m}} \right)}}}}}};$

wherein p(x|m) is the probability density basis function of Gaussianmixture model, a represents a standard deviation of the Gaussianprobability density function, w represents the weight value of overflowpoint, and the value range is 0-1, x is translation variable.

-   -   Calculating the minimum negative logarithm likelihood function:        E(θ,σ²)=−Σ_(n=1) ^(N) log Σ_(m=1) ^(M+1) P(m)p(x|m),

wherein θ represents transformation parameters, and a represents thestandard deviation of the Gaussian probability density function.

According to the gradient descent method, the derivative can beobtained:

${{Q\left( {\theta,\sigma^{2}} \right)} = {{\frac{1}{2\sigma^{2}}{\sum\limits_{n = 1}^{N}{\sum\limits_{m = 1}^{M}{{P^{old}\left( {m{❘x_{n}}} \right)}{{x_{n} - {\tau\left( {y_{m},\theta} \right)}}}^{2}}}}} + {\frac{N_{p}D}{2}\log\sigma^{2}}}},$

wherein

${{P(m)} = \frac{1}{M}},$${N_{P} = {{\sum_{n = 1}^{N}{\sum_{m = 1}^{M}{P^{old}\left( {m{❘x_{n}}} \right)}}} \leq N}},$${{P^{old}\left( {m{❘x_{n}}} \right)} = \frac{\exp^{{- \frac{1}{2}}{\frac{x_{n} - {\tau({y_{m},\theta^{old}})}}{\sigma^{old}}}^{2}}}{{\sum_{k = 1}^{M}\exp^{{- \frac{1}{2}}{\frac{x_{n} - {\tau({y_{k},\theta^{old}})}}{\sigma^{old}}}^{2}}} + c}},$

r represents one of rigid, affine and non-rigid transformations, and

$c = {\left( {2{\prod\sigma^{2}}} \right)^{D/2}\frac{\omega}{1 - \omega}{\frac{M}{N}.}}$

The optimal parameters of the model of the minimum negative logarithmlikelihood function are solved by Expectation-Maximization algorithm(EM). Finally, according to the selected point cloud data andtransformation parameters, the location of the preoperative liver regionmap corresponding to CT/MRI scan sequence image after operation iscalculated.

Through the registration of liver, the registration of liver tumorablation area and liver tumor can be achieved indirectly. Theregistration result is shown in FIG. 3, 1 is the inactivated tumorresidual image, 2 is the preoperative tumor registration transformationresult, and 3 is the postoperative ablation area. It can be seen thatthe preliminary registration result is different from the actual tumormorphology.

Step 5, the medical image map before ablation, the preoperative liverregion map, the preoperative liver tumor region map and the registrationresult map are used as the input of U-net network, and the postoperativeliver tumor residual image map is used as the real training label. Thenetwork is trained by the random gradient descent method to obtain theliver tumor prediction model. The prediction model of liver tumorobtained is as follows: the medical image before ablation, thepreoperative liver region map, the preoperative liver tumor region mapand the registration result map are composed of four channels of imagedata, and then input into U-net for coding to obtain the pixelclassification probability map. The pixels with probability≥0.5 in thepixel classification probability map are determined as liver tumors, andthe pixels with probability<0.5 are determined as background, andfinally the predicted liver tumor region is obtained.

As shown in FIG. 4 , a pixel classification probability map is obtained,specially: the medical image map before ablation, the preoperative liverregion map, the preoperative liver tumor region map and the registrationresult map are composed of four channels of image data, and then inputinto U-net for coding. The input data is convoluted twice, and thenumber of output channels of each convolution is 64, and the feature mapf1 is obtained, which is performed for maximum pooling. After twoconvolutions, the number of output channels of each convolution is 128,and the feature map f2 is obtained, which is performed for maximumpooling. After two convolutions, the number of output channels of eachconvolution is 256, and the feature map f3 is obtained, which isperformed for maximum pooling. After two convolutions, the number ofoutput channels of each convolution is 512, and the feature map f4 isobtained, which is performed for maximum pooling. After twoconvolutions, the number of output channels of each convolution is 1024,the feature map f5 is obtained, which is the encoding process. Then, f5is decoded, that is, f5 is up sampled and performed concat with f4.After two convolutions, the number of output channels of eachconvolution is 512, and the feature map f4_1 is obtained, f4_1 is upsampled and performed concat with f3. After two convolutions, the numberof output channels of each convolution is 256, and the feature map f3_1is obtained. F3_1 is up sampled and performed concat with f2. After twoconvolutions, the output channel of each convolution is 128, and thefeature map f2_1 is obtained. F2_1 is up sampled and performed concatwith f1. After two convolutions, the number of output channels of eachconvolution is 64, and after another convolution, the number of outputchannels is 2 to obtain the pixel classification probability map; theconcat is to connect the two feature maps in the channel dimension.

The convolution is specifically as follows: X_(j) ^(l)=ƒ(Σ_(i∈M) _(j)x_(i) ^(l−1)*k_(ij) ^(l)+b_(j) ^(l)), wherein x represents an inputcharacteristic channel of the accretion layer, X represents an outputcharacteristic channel of the accretion layer, k represents a parameterof the convolution layer, b represents an offset term of the convolutionlayer;

the symbol * represents the convolution operator; l represents thenumber of layers, i represents the i^(th) neuron node of l−1 layer; jrepresents the j^(th) neuron node of l layer; M_(j), represents the setof selected input characteristic graphs; x_(i) ^(l−1) refers to theoutput of l−1 layer as the input of l layer; ƒ represents the activationfunction; the maximum pooling is to select the maximum value in a regionto represent the characteristics of the region.

Dice loss function is constructed in the process of network training toalleviate the imbalance of background and foreground pixels. The Diceloss function is as follows:

${d = {1 - {2\frac{pt}{p + t}}}},$wherein p represents the predicted liver tumor region and t representsthe real liver tumor region. The loss of a back propagation in thetraining process includes the loss calculated by the deviation betweenthe predicted tumor location and the real location.

Step 6, the morphological changes of liver tumor after ablation ispredicted with the liver tumor prediction model. According to thegrouping of data sets in Step 2, multiple U-net network models can beobtained, and then input into the corresponding network model accordingto the basic liver morphology, tumor type and pathological type, andpredict the tumor changes.

According to the changes of tumor and the actual tumor, the tumormorphology is obtained by modifying Step 4. The result of tumordeformation correction is shown in FIG. 5 . 5 is the residual image ofinactivated tumor, 4 is the result of preoperative tumor registrationtransformation, 6 is the ablation area after operation. It can be seenthat the modified tumor deformation is basically consistent with theactual shape after inactivation.

The invention provides a method for predicting the morphological changesof liver tumor after ablation based on deep learning, by which solvesthe problem of curative effect evaluation after ablation. It predictsthe morphological changes of liver tumor after ablation with CPD pointset registration and U-net network. Through the registration of livercontour before and after ablation, the transformation relationship isobtained, and then the location of preoperative liver tumor inpostoperative CT/MRI image is obtained. Finally, the morphologicalchanges of liver tumor after ablation are predicted by U-net network.The invention can predict the morphological changes of liver tumor afterablation, provide the basis for quantitatively evaluating whether theablation area completely covers the tumor, facilitate the doctor toaccurately evaluate the postoperative curative effect, and lay thefoundation for the follow-up treatment plan of the patient.

The invention is not limited to the above optional embodiments, andanyone can obtain various other forms of products under theenlightenment of the invention. The above specific embodiments shouldnot be understood as limiting the scope of protection of the invention.The scope of protection of the invention should be defined in theclaims, and the specification can be used to explain the claims.

What is claimed is:
 1. A method for predicting morphological changes ofliver tumor after ablation based on deep learning, comprising: 1)Obtaining a data set comprising a medical image of the liver tumorbefore the ablation and a medical image of the liver tumor after theablation; 2) Pre-processing the medical image of the liver tumor beforethe ablation and the medical image of the liver tumor after theablation; 3) Obtaining a preoperative liver region map and apreoperative liver tumor region map from a medical image map before theablation, and obtaining a postoperative liver region map, apostoperative ablation region map, and a postoperative liver tumorresidual image map from a medical image map after the ablation; 4)Registering the preoperative liver region map and the postoperativeliver region map by a Coherent Point Drift (CPD) algorithm, andobtaining a transformation matrix; obtaining a registration result mapof the medical image map after the ablation corresponding to thepreoperative liver region map and the preoperative liver tumor regionmap according to the transformation matrix; 5) using the medical imagemap before the ablation, the preoperative liver region map, thepreoperative liver tumor region map and the registration result map asan input of U-net network, and using the postoperative liver tumorresidual image map as a real training label; training the U-net networkby a random gradient descent method to obtain a liver tumor predictionmodel; and 6) using the liver tumor prediction model to predict themorphological changes of the liver tumor after the ablation; whereinstep 3 comprises: marking the preoperative liver region map, thepreoperative liver tumor region map, and the postoperative liver regionmap by a maximum flow/minimum cut algorithm; introducing a potentialenergy field function based on a global and local region representationas a constraint in a segmentation process, and establishing an adaptivehybrid variational model; using the maximum flow/minimum cut algorithmto solve an energy equation minimization; determining a target regionselectively according to gray information, boundary gradient, textureinformation and local context information in different image regions;wherein step 4 comprises: obtaining liver data point set of thepreoperative liver region map and the postoperative liver region map,making the liver data point set of the preoperative liver region mapX_(i)=(x₁, . . . , x_(N))^(T) as a target point set, and making theliver data point set of the postoperative liver region map Y_(i)=(y₁, .. . , y_(M))^(T) as a model point set, wherein the target point set is adata set of Gaussian mixture model, and the model point set is a kernelpoint set of the Gaussian mixture model; N and M respectively representa number of points in the target point set and a number of the modelpoint set; a probability density function of the Gaussian mixture modelis:${p\left( {x{❘m}} \right)} = {\frac{1}{\left( {2{\prod\sigma^{2}}} \right)^{D/2}}\exp^{\frac{{{x - y_{m}}}_{2}^{2}}{2\sigma^{2}}}}$${{p(x)} = {{\omega\frac{1}{N}} + {\left( {1 - \omega} \right){\sum\limits_{m = 1}^{M}{\frac{1}{M}{p\left( {x{❘m}} \right)}}}}}};$wherein p(x|m) is a probability density basis function of the Gaussianmixture model, σ represents a standard deviation of the probabilitydensity function, ω represents a weight value of overflow points, and avalue range is 0-1, x is a translation variable; calculating a minimumnegative logarithm likelihood function:E(θ,σ²)=−Σ_(n=1) ^(N) log Σ_(m=1) ^(M+1) P(m)p(x|m), θ representstransformation parameters, and σ represents the standard deviation ofthe probability density function; obtaining a derivative according to agradient descent method:${{Q\left( {\theta,\sigma^{2}} \right)} = {{\frac{1}{2\sigma^{2}}{\sum\limits_{n = 1}^{N}{\sum\limits_{m = 1}^{M}{{P^{old}\left( {m{❘x_{n}}} \right)}{{x_{n} - {\tau\left( {y_{m},\theta} \right)}}}^{2}}}}} + {\frac{N_{p}D}{2}\log\sigma^{2}}}},$wherein ${{P(m)} = \frac{1}{M}},$${N_{P} = {{\sum_{n = 1}^{N}{\sum_{m = 1}^{M}{P^{old}\left( {m{❘x_{n}}} \right)}}} \leq N}},$${{P^{old}\left( {m{❘x_{n}}} \right)} = \frac{\exp^{{- \frac{1}{2}}{\frac{x_{n} - {\tau({y_{m},\theta^{old}})}}{\sigma^{old}}}^{2}}}{{\sum_{k = 1}^{M}\exp^{{- \frac{1}{2}}{\frac{x_{n} - {\tau({y_{k},\theta^{old}})}}{\sigma^{old}}}^{2}}} + c}},$τ represents one of rigid, affine and non-rigid transformations, and${c = {\left( {2{\prod\sigma^{2}}} \right)^{D/2}\frac{\omega}{1 - \omega}\frac{M}{N}}};$solving optimal parameters for the transformation parameters of theminimum negative logarithm likelihood function by iteration with anExpectation-Maximization algorithm; and calculating a location of thepreoperative liver region map corresponding to the medical image mapafter the ablation according to selected point cloud data and theoptimal parameters for the transformation parameters, wherein theselected point cloud data comprise the liver data point set of thepreoperative liver region map X_(i)=(x₁, . . . , X_(N))^(T) and theliver data point set of the postoperative liver region map Y_(i)=(y₁, .. . , y_(M))^(T).
 2. The method according to claim 1, wherein themedical images each comprises an image obtained from computed tomography(CT) and magnetic resonance imaging (MRI).
 3. The method according toclaim 1, wherein step 2 comprises: dividing the data set into groupsaccording to liver influencing factors, then reading the medical imageof the liver tumor before the ablation and the medical image of theliver tumor after the ablation, and processing the medical image of theliver tumor before the ablation and the medical image of the liver tumorafter the ablation by Gaussian de-noising, gray histogram equalization,image contrast enhancement, rotation, flipping and data standardization.4. The method according to claim 3, wherein the liver influencingfactors comprise a liver state, a tumor type, and a pathological type.5. The method according to claim 1, wherein step 5 comprises: composinginput data comprising four channels of image data with the medical imagebefore the ablation, the preoperative liver region map, the preoperativeliver tumor region map, and the registration result map, and theninputting into the U-net network for coding to obtain a pixelclassification probability map; determining pixels with probability≥0.5in the pixel classification probability map as the liver tumor, andpixels with probability<0.5 as background, and finally obtaining apredicted liver tumor region.
 6. The method according to claim 5,wherein the step of obtaining the pixel classification probability mapcomprises: composing the input data comprising the four channels of theimage data with the medical image before the ablation, the preoperativeliver region map, the preoperative liver tumor region map, and theregistration result map, and then inputting the input data into theU-net network for coding; convoluting the input data twice, and a numberof output channels of each convolution is 64, and obtaining a featuremap f1 for maximum pooling; convoluting twice, the number of outputchannels of each convolution is 128, and obtaining a feature map f2 forthe maximum pooling; convoluting twice, the number of output channels ofeach convolution is 256, and obtaining a feature map f3 for the maximumpooling; convoluting twice, the number of output channels of eachconvolution is 512, and obtaining a feature map f4 for the maximumpooling; convoluting twice, the number of output channels of eachconvolution is 1024, and obtaining a feature map f5 for an encodingprocess; decoding the feature map f5, comprising sampling the featuremap f5 and performing concat with the feature map f4, after convolutingtwice, the number of output channels of each convolution is 512, andobtaining a feature map f4_1; up sampling the feature map f4_1, andperforming concat with the feature map f3, after convoluting twice, thenumber of output channels of each convolution is 256, and obtaining afeature map f3_1; up sampling the feature map f3_1, and performingconcat with the feature map f2, after convoluting twice, the number ofoutput channels of each convolution is 128, and obtaining a feature mapf2_1; up sampling the feature map f2_1, and performing concat with thefeature map f1, after convoluting twice, the number of output channelsof each convolution is 64, and after convoluting once, the number ofoutput channels is 2 to obtain the pixel classification probability map;wherein the concat is to connect two feature maps in a channeldimension.
 7. The method according to claim 6, wherein each of theconvoluting steps is based on X_(j) ^(l)=ƒ(Σ_(i∈M) _(j) x_(i)^(l−1)*k_(ij) ^(l)+b_(j) ^(l)), wherein x represents an inputcharacteristic channel of an accretion layer, X represents an outputcharacteristic channel of the accretion layer, k represents a parameterof a convolution layer, b represents an offset term of the convolutionlayer, the symbol * represents a convolution operator, l represents anumber of layers, i represents i^(th) neuron node of l−1 layer, jrepresents j^(th) neuron node of l layer, M_(j) represents a set ofselected input characteristic graphs, x_(i) ^(l−1) refers to an outputof the l−1 layer as an input of the l layer, ƒ represents an activationfunction, and the maximum pooling is to select a maximum value in aregion to represent characteristics of the region.
 8. The methodaccording to claim 7, wherein step 5 comprises constructing Dice lossfunction in a process of training the U-net network to alleviate animbalance of background and foreground pixels, wherein the Dice lossfunction is as follows: ${d = {1 - {2\frac{pt}{p + t}}}},$ wherein prepresents the predicted liver tumor region and t represents a realliver tumor region.